Methods for estimating a conditional distribution function
AbstractMotivated by the problem of setting prediction intervals in time series analysis, we suggest two new methods for conditional distribution estimation. The first method is based on locally fitting a logistic model and is in the spirit of recent work on locally parametric techniques in density estimation. It produces distribution estimators that may be of arbitrarily high order but nevertheless always lie between 0 and 1. The second method involves an adjusted form of the Nadaraya--Watson estimator. It preserves the bias and variance properties of a class of second-order estimators introduced by Yu and Jones but has the added advantage of always being a distribution itself. Our methods also have application outside the time series setting; for example, to quantile estimation for independent data. This problem motivated the work of Yu and Jones.
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Bibliographic InfoPaper provided by School of Economics and Finance, Queensland University of Technology in its series School of Economics and Finance Discussion Papers and Working Papers Series with number 208l.
Date of creation: 15 Jun 2006
Date of revision:
Absolutely regular; bandwidth; biased bootstrap; conditional distribution; kernel methods; local linear methods; local logistic methods; Nadaraya-Watson estimator; prediction; quantile estimation; time series analysis; weighted bootstrap;
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- Norman R. Swanson & Valentina Corradi & Walter Distaso, 2011.
"Predictive Inference for Integrated Volatility,"
Departmental Working Papers
201109, Rutgers University, Department of Economics.
- Norman R. Swanson & Valentina Corradi & Walter Distaso, 2011. "Predictive Inference for Integrated Volatility," Departmental Working Papers 201108, Rutgers University, Department of Economics.
- Valentina Corradi & Norman Swanson & Walter Distaso, 2006. "Predictive Inference for Integrated Volatility," Departmental Working Papers 200616, Rutgers University, Department of Economics.
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